Comparative GO: a web application for comparative gene ontology and gene ontology-based gene selection in bacteria
Article
Article Title | Comparative GO: a web application for comparative gene ontology and gene ontology-based gene selection in bacteria |
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ERA Journal ID | 39745 |
Article Category | Article |
Authors | Fruzangohar, Mario (Author), Ebrahimie, Esmaeil (Author), Ogunniyi, Abiodun D. (Author), Mahdi, Layla K. (Author), Paton, James C. (Author) and Adelson, David L. (Author) |
Journal Title | PLoS One |
Journal Citation | 8 (3), pp. e58759-e58767 |
Number of Pages | 9 |
Year | 2013 |
Publisher | Public Library of Science (PLoS) |
Place of Publication | United States |
ISSN | 1932-6203 |
Digital Object Identifier (DOI) | https://doi.org/10.1371/journal.pone.0058759 |
Web Address (URL) | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594149/pdf/pone.0058759.pdf |
Abstract | The primary means of classifying new functions for genes and proteins relies on Gene Ontology (GO), which defines genes/proteins using a controlled vocabulary in terms of their Molecular Function, Biological Process and Cellular Component. The challenge is to present this information to researchers to compare and discover patterns in multiple datasets using visually comprehensible and user-friendly statistical reports. Importantly, while there are many GO resources available for eukaryotes, there are none suitable for simultaneous, graphical and statistical comparison between multiple datasets. In addition, none of them supports comprehensive resources for bacteria. By using Streptococcus pneumoniae as a model, we identified and collected GO resources including genes, proteins, taxonomy and GO relationships from NCBI, UniProt and GO organisations. Then, we designed database tables in PostgreSQL database server and developed a Java application to extract data from source files and loaded into database automatically. We developed a PHP web application based on Model-View-Control architecture, used a specific data structure as well as current and novel algorithms to estimate GO graphs parameters. We designed different navigation and visualization methods on the graphs and integrated these into graphical reports. This tool is particularly significant when comparing GO groups between multiple samples (including those of pathogenic bacteria) from different sources simultaneously. Comparing GO protein distribution among up- or downregulated genes from different samples can improve understanding of biological pathways, and mechanism(s) of infection. It can also aid in the discovery of genes associated with specific function(s) for investigation as a novel vaccine or therapeutic targets. |
Related Output | |
Is original form of | Correction: Comparative GO: A Web Application for Comparative Gene Ontology and Gene Ontology-Based Gene Selection in Bacteria |
ANZSRC Field of Research 2020 | 310704. Microbial genetics |
460103. Applications in life sciences | |
Byline Affiliations | University of Adelaide |
Institution of Origin | University of Southern Queensland |
Funding source | NHMRC Grant ID 627142 |
Funding source | NHMRC Grant ID 565526 |
https://research.usq.edu.au/item/q35x1/comparative-go-a-web-application-for-comparative-gene-ontology-and-gene-ontology-based-gene-selection-in-bacteria
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